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This thesis describes a Decision-Centric traceability framework that supports software engineering activities such as architectural preservation, impact analysis, and visualization of design intent. We present a set of traceability patterns, derived from studying real-world architectural designs in high-assurance and highperformance systems. We further present a trace-retrieval approach that reverse engineers design decisions and their associated traceability links by training a classifier to recognize fragments of design decisions and then using the traceability patterns to reconstitute the decisions from their individual parts. traceability meta-models [2] for tracing ASRs can lead to the proliferation of a large number of unmanageable traceability links. This research presents a Decision-Centric Traceability (DCT) approach for tracing between ASRs and architectural components, and is designed to help developers ensure long-term integrity of the system-level qualities. In comparison to existing traceability meta-models [2], DCT reduces the number of traceability links and provides semantically rich traceability links that are used to support critical software engineering activities such as architectural preservation, impact analysis, and design visualization.